搜索资源列表
Kmeans
- 基于opencv的kmeans聚类实现 输入文本数据,进行聚类输出-Opencv-based clustering of kmeans the input text data, clustering output
colorSignature
- 用openCV提取图片的象素值,用聚类进行分割,找出各个聚类的中心点,聚类个数是动态决定的,可重新设置个数最大上限。-OpenCV extract images using pixel value segmentation by clustering to identify the center of each cluster, clustering is a dynamic number of decisions can be re-set the upper limit of the num
MARK_Kmeans
- 使用k-means算法对一副RGB色彩空间的图像作简单的聚类。根据命令行提示输入聚类的大小K,程序自动计算每一个像素点的归属并着色该点为该类的色彩均值。工程运行于VS2008环境,需要OpenCV支持。Debug目下exe文件可以直接双击运行查看结果。-Using k-means algorithm on an RGB color space images to make a simple clustering. According to the command prompt enter the
color-cluster
- 基于opencv的图像颜色聚类算法。聚类精度较高,但颜色数目需要输入。-Opencv image-based color clustering algorithm. Clustering high accuracy, but the number of colors required to enter.
CPPBuilder-photo-dector
- C 调用opencv,边缘检测,图像分割,插值拟合,聚类变换,图像修改-C call opencv, edge detection, image segmentation, interpolation fitting, clustering transform, image modification
PeopleDensitydll
- 视频图像的人群密度检测,多种人群密度场景下人群计数算法: 算法功能:建立图像特征和图像人数的数学关系 算法输入:训练样本图像1,2…K 算法输出:模型估计参数 ,参考图像 算法流程:1)对训练样本图像进行分块处理(算法1.1); 2)通过算法1.2,计算训练样本各个对应分块的ALBP特征归一化,再用K-means算法(可使用opencv等算法库实现,不再描述其算法),将图像块分成k(k<K)类,获取k(k<K)个聚类中心,即为参考图像; 3)对分块的图像进行与
HarrisDetector
- Harris角点检测是一种经典的角点检测方法,该代码中包含 1.原始的直接调用OpenCV实现角点检测; 2.封装自定义类来改进角点检测; 3.实现了增加容忍距离解决特征点聚类是角点分布均匀-Harris corner detection is a classical corner detection method, the code contained 1. The original of directly calling OpenCV corner detection to
optical
- opencv写的基于光流法的运动人流分类,能够实现读取视频,实现金字塔光流法和HS光流法,最后对流动人群进行有效的聚类。-Opencv writing movement flow classification based on optical flow method, read the video, can be implemented to realize pyramid optical flow method and the HS optical flow method, finally,
1K_means
- 用K-means算法将点进行聚类,点以结构体的形式表示,opencv+vs2010跑通。-By K-means clustering algorithm point, the point structure in the form of representation, opencv+ vs2010 run through.
1EMalgorithm
- 利用期望最大化聚类算法,从txt文件中读如200+个点的坐标,并将这些点尽心聚类。开发环境为opencv+vs2010-Expectation Maximization clustering algorithm, txt file read coordinates 200+ points, and these points dedicated cluster. opencv+ vs2010
colorSignature
- 用openCV提取图片的象素值,用聚类进行分割,找出各个聚类的中心点,聚类个数是动态决定的,可重新设置个数最大上限。-OpenCV extract images using pixel value segmentation by clustering to identify the center of each cluster, clustering is a dynamic number of decisions can be re-set the upper limit of the num
KMkeen
- 基于人类视觉将图像分割成若干个有意义的区域是目标检测和模式识别的基础。图像分割属于图像处理中一种重要的图像分析技术。图像分割的基本方法是对灰度图像分割,处理图像的亮度分量,简单快速。本论文介绍了传统的图像分割与K-均值聚类算法分割,然后利用OpenCV函数将其实现,并介绍了OpenCV中图像分割相关的基本函数。-Based on the human visual image is segmented into several meaningful regions is the basis for
K_means
- K均值聚类算法 属于模式识别的一种基本算法 在此没有与opencv关联-K-means clustering algorithm A basic pattern recognition algorithm is not associated with this opencv
kMeans
- Kmeans聚类算法,采用Opencv开源机器视觉库-Kmeans clustering algorithm, using Opencv computer Vision library
kmeas_cPP
- K-means聚类算法 依赖 Eigen和opencv-K-means clustering algorithm relies Eigen and opencv
MFCVIDEO
- 基于opencv的运动人流分类,采用了帧差法、DBSCAN聚类算法实现-Based on opencv motion flow classification, using the frame difference, DBSCAN clustering algorithm
ap
- opencv hierachical clustering demo
clust.py.tar
- this is clustering based on image segmentation in python and opencv.
slic
- Simple Linear Iterative Clustering(SLIC) 算法实现; 需要添加 Opencv-Simple Linear Iterative Clustering (SLIC) algorithm we need to add Opencv
Python计算机视觉编程
- 本书介绍Python的计算机视觉编程,用清晰的Python示例,细致讲解对象识别、3D重建、立体图像、增强现实及其他计算机视觉应用技巧,给出了分析图像的工具与方法。内容:机器人导航、医学图像分析;图像映射与变换;多图像的3D重建;用聚类方法基于相似性和内容组织图像;基于视觉内容的图像检索技巧;实现图像内容分类和对象识别的算法;通过Python接口访问常用的OpenCV库。(This book introduces Python's computer vision programming. Wit